Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants.

BACKGROUND: Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants. OBJECTIVE: We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques. METHOD: Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species. RESULT: A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration. CONCLUSION: We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

Charge of a transmembrane peptide alters its interaction with lipid membranes.

Transmembrane (TM) proteins interact closely with the surrounding membrane lipids. Lipids in the vicinity of TM proteins were reported to have hindered mobility, which has been associated with lipids being caught up in the rough surface of the TM domains. These reports, however, neglect one important factor that largely influences the membrane behavior - electrostatics of the TM peptides that are usually positively charged at their cytosolic end. Here, we study on the example of a neutral and a positively charged WALP peptide, how the charge of a TM peptide influences the membrane. We investigate both its dynamics and mechanics by: (i) time dependent fluorescent shift in combination with classical and FRET generalized polarization to evaluate the mobility of lipids at short and long-range distance from the peptide, (ii) atomic force microscopy to observe the mechanical stability of the peptide-containing membranes, and (iii) molecular dynamics simulations to analyze the peptide-lipid interactions. We show that both TM peptides lower lipid mobility in their closest surroundings. The peptides cause lateral heterogeneity in lipid mobility, which in turn prevents free lipid rearrangement and lowers the membrane ability to seal ruptures after mechanical indentations. Introduction of a positive charge to the peptide largely enhances these effects, affecting the whole membrane. We thus highlight that unspecific peptide-lipid interactions, especially the electrostatics, should not be overlooked as they have a great impact on the mechanics and dynamics of the whole membrane.

Genome-wide identification of microsatellites for mapping, genetic diversity and cross-transferability in wheat (Triticum spp).

Wheat (Triticum aestivum L.) is a crucial global staple crop, and is consistently being improved to enhance yield, disease resistance, and quality traits. However, the development of molecular markers is a challenging task due to its hexaploid genome. Molecular marker system such as simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) are helpful for breeding, but SNP has limitations due to its development cost and its conversion to breeder markers. The study proposed an in-silico approach, by utilizing the low-cost transcriptome sequencing of two parental lines, 'TAC 75' and 'WH 1105', to identify polymorphic SSRs for mapping in a recombinant inbred line (RIL) population. This study introduces a new approach to bridge wheat genetics intricacies and next-generation sequencing potential. It presents a comprehensive genome-wide SSR distribution using IWGSC CS RefSeq v2.1 genome assembly and to identify 189 polymorphic loci through in-silico strategy. Of these, 54.76% showed polymorphism between parents, surpassing the traditional low polymorphic success rate. A RIL population screening validated these markers, demonstrating the fitness of identified markers through chi-square tests. The designed SSRs were also validated for genetic diversity analysis in a subset of 37 Indian wheat genotypes and cross-transferability in the wild/relative wheat species. In diversity analysis, a subset of 38 markers revealed 95 alleles (2.5 allele/locus), indicating substantial genetic variation. Population structure analysis unveiled three distinct groups, supported by phylogenetic and PCoA analyses. Further the polymorphic SSRs were also analyzed for SSR-gene association using gene ontology analysis. By utilizing the developing seed transcriptome data within parental lines, the study has enhanced the polymorphic SSR identification precision and facilitated in the RIL population. The undertaken study pioneers the use of transcriptome sequencing and genetic mapping to overcome challenges posed by the intricate wheat genome. This approach offers a cost-effective, less labour-intensive alternative to conventional methods, providing a platform for advancing wheat breeding research.

FRET-GP - A Local Measure of the Impact of Transmembrane Peptide on Lipids.

Reconstitution of a transmembrane protein in model lipid systems allows studying its structure and dynamics in isolation from the complexity of the natural environment. This approach also provides a well-defined environment for studying the interactions of proteins with lipids. In this work, we describe the FRET-GP method, which utilizes Förster resonance energy transfer (FRET) to specifically probe the nanoenvironment of a transmembrane domain. The tryptophan residues flanking this domain act as efficient FRET donors, while Laurdan acts as acceptor. The fluorescence of this solvatochromic probe is quantified using generalized polarization (GP) to report on lipid mobility in the vicinity of the transmembrane domain. We applied FRET-GP to study the transmembrane peptide WALP incorporated in liposomes. We found that the direct excitation of Laurdan to its second singlet state strongly contributes to GP values measured in FRET conditions. Removal of this parasitic contribution was essential for proper determination of GPFRET - the local analogue of classical GP parameter. The presence of WALP significantly increased both parameters but the local effects were considerably stronger (GPFRET ≫ GP). We conclude that WALP restricts lipid movement in its vicinity, inducing lateral inhomogeneity in membrane fluidity. WALP was also found to influence lipid phase transition. Our findings demonstrated that FRET-GP simultaneously provides local and global results, thereby enhancing the depth of information obtained from the measurement. We highlight the simplicity and sensitivity of the method, but also discuss its potential and limitations in studying protein-lipid interactions.

A Rare Case of Pulmonary Cavitary Disease Caused by Mycobacterium xenopi.

Mycobacterium xenopi is a slow-growing, acid-fast, non-tuberculous mycobacterium (NTM). It is often considered to be a saprophyte or an environmental contaminant. Mycobacterium xenopi has low pathogenicity and is usually seen in patients with pre-existing chronic lung diseases and immunocompromised patients. We present a case of Mycobacterium xenopi causing a cavitary lesion in a patient with chronic obstructive pulmonary disease (COPD) that was discovered incidentally during the low-dose CT scan done for lung cancer screening in a patient with COPD. The initial workup was negative for NTM. An Interventional-guided (IR) core needle biopsy was done given the high suspicion for NTM and revealed a positive culture for Mycobacterium xenopi.  Our case highlights the importance of considering NTM in the differential diagnosis of at-risk patients and pursuing invasive testing if there is a high clinical suspicion.

Tick-Borne Rhabdomyolysis: A Rare Case of Rhabdomyolysis and Acute Kidney Injury Due to Anaplasmosis.

Anaplasmosis is a tick-borne illness commonly seen in the northeastern states of the United States. The most common presenting signs are fever, malaise, and body aches accompanied by leukopenia, thrombocytopenia, and transaminitis. Rhabdomyolysis and acute kidney injury are rare presentations that can lead to significant morbidity.  We present the case of a patient who presented with non-specific symptoms of malaise, fatigue, and body aches and was found to have rhabdomyolysis and acute kidney injury on laboratory workup. A presumptive diagnosis of anaplasmosis was made, and the patient was started on treatment for the same. The patient recovered successfully. Our case highlights the rare presentation of anaplasmosis with rhabdomyolysis and acute kidney injury. Physician awareness is needed for early diagnosis and preventing morbidity.

Continuous manufacturing of monoclonal antibodies: Dynamic control of multiple integrated polishing chromatography steps using BioSMB.

We propose a strategy for automation and control of multi-step polishing chromatography in integrated continuous manufacturing of monoclonal antibodies. The strategy is demonstrated for a multi-step polishing process consisting of cation exchange chromatography in bind-and-elute mode followed by mixed-mode chromatography in flowthrough mode. A BioSMB system with a customized Python control layer is used for automation and scheduling of both the chromatography steps. Further, the BioSMB valve manifold is leveraged for in-line conditioning between the two steps, as tight control of pH and conductivity is essential when operating with multimodal resins because even slight fluctuations in load conditions adversely affect the chromatography performance. The pH and conductivity of the load to the multimodal chromatography columns is consistent, despite the elution gradient of the preceding cation exchange chromatography step. Inputs from the BioSMB pH and conductivity sensors are used for real-time control of the 7 pumps and 240 valves to achieve in-line conditioning inside the BioSMB manifold in a fully automated manner. This is confirmed by showcasing different elution strategies in cation exchange chromatography, including linear gradient, step gradient and process deviations like tubing leakage. In all the above cases, the model was able to maintain the pH and conductivity of multimodal chromatography load within the range of 6 ± 0.1 pH and 7 ± 0.3 mS/cm conductivity. The strategy eliminates the need for using multiple BioSMB units or integrating external pumps, valves, mixers, surge tanks, or sensors between the two steps as is currently the standard approach, thus offering a simple and robust structure for integrating multiple polishing chromatography steps in continuous downstream monoclonal antibody purification trains.

Artificial intelligence and machine learning applications in biopharmaceutical manufacturing.

Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.

Continuous integrated manufacturing for biopharmaceuticals: A new paradigm or an empty promise?

Continuous integrated bioprocessing has elicited considerable interest from the biopharma industry for the many purported benefits it promises. Today many major biopharma manufacturers around the world are engaged in the development of continuous process platforms for their products. In spite of great potential, the path toward continuous integrated bioprocessing remains unclear for the biologics industry due to legacy infrastructure, process integration challenges, vague regulatory guidelines, and a diverging focus toward novel therapies. In this article, we present a review and perspective on this topic. We explore the status of the implementation of continuous integrated bioprocessing among biopharmaceutical manufacturers. We also present some of the key hurdles that manufacturers are likely to face during this implementation. Finally, we hypothesize that the real impact of continuous manufacturing is likely to come when the cost of manufacturing is a substantial portion of the cost of product development, such as in the case of biosimilar manufacturing and emerging economies.

Continuous manufacturing of monoclonal antibodies: Automated downstream control strategy for dynamic handling of titer variations.

Handling long-term dynamic variability in harvest titer is a critical challenge in continuous downstream manufacturing. This challenge is becoming increasingly important with the advent of high-titer clones and modern upstream perfusion processes where the titer can vary significantly across the course of a campaign. In this paper, we present a strategy for real-time, dynamic adjustment of the entire downstream train, including capture chromatography, viral inactivation, depth filtration, polishing chromatography, and single-pass formulation, to accommodate variations in titer from 1-7 g/L. The strategy was tested in real time in a continuous downstream purification process of 36 h duration with induced titer variations. The dynamic control strategy leverages real-time NIR-based concentration sensors in the harvest material to continuously track the titer, integrated with an in-house Python-based control system that operates a BioSMB for carrying out capture and polishing chromatography, as well as a series of pumps and solenoid valves for carrying out viral inactivation and formulation. A set of 9 different methods, corresponding to the different harvest titers have been coded onto the Python controller. The methods have a varying number of chromatography columns (3-6 for Protein A and 2-10 for CEX), designed to ensure proper scheduling and optimize productivity across the entire titer variation space. The approach allows for a wide range of titers to be processed on a single integrated setup without having to change equipment or to re-design each time. The strategy also overcomes a key unexplored challenge in continuous processing, namely hand-shaking the downstream train to upstream conditions with long-term titer variability while maintaining automated operation with high productivity and robustness.

Process Analytical Technology (PAT) Implementation for Membrane Operations in Continuous Manufacturing of mAbs: Model-Based Control of Single-Pass Tangential Flow Ultrafiltration.

Control of single pass tangential flow ultrafiltration (SPTFF) is crucial for continuous manufacturing of monoclonal antibodies (mAbs). Integrating SPTFF technology into continuous manufacturing trains requires successful resolution of several challenges that arise due to the complexity of mass transfer interactions across multi-membrane configurations, the significant effect of feed material attributes and process variability on flux, and the need for advanced scheduling. In this paper, we propose a real-time, automated monitoring and control strategy for SPTFF in continuous processing of mAbs. The approach leverages a previously developed model for predicting the VCF across an SPTFF module based on the gel polarization model of protein ultrafiltration. A distributed control system (DCS) architecture was created for integrating the monitoring sensors and control elements, including NIRS sensors for concentration monitoring, as well as weighing balances, pressure sensors, pumps, and valves. Two different SPTFF control strategies were developed, firstly for final formulation of the drug product into the drug substance (ultrafiltration and diafiltration), and secondly for in-line concentration between two chromatography steps. Case studies were designed with 15 runs to test the strategy with a range of deviations induced in the feed and process conditions. The retentate concentration was controlled to within 10% of the target value in all runs. The combination of real-time sensor data and model-based control effectively enabled automated and tightly controlled operation of the SPTFF step and is a key enabler of quality by design in continuous mAb manufacturing.

Process analytical technology in continuous processing: Model-based real time control of pH between capture chromatography and viral inactivation for monoclonal antibody production.

A real time mechanistic model-based control strategy is demonstrated for in-line pH adjustment post-capture chromatography and prior to viral inactivation for continuous processing of monoclonal antibodies. At this point in the process, tight control of pH is essential, as pH fluctuations above 3.5 can result in incomplete viral inactivation, while fluctuations below 3.5 can lead to significant aggregate formation. The present approach predicts the pH profile during the transition phase between chromatography wash and elution steps by modelling the process stream at the column outlet as a mixture of two independent buffer systems. Control of pH in this transition phase is a critical consideration in capture chromatography as a significant amount of mAb material is eluted at this time. The model inputs are buffer concentrations, flow rates, and theoretical pKa values, along with cleaning step conductivity profiles which are readily available from a typical process chromatography equipment. The utilization of the most recent cleaning cycle data as an input to the model allows sensitive calibration to the individual process at hand on a column-to-column basis. The model is able to accurately predict the pH profile throughout the elution, as well as calculate the flow rate of the acid (titrant) required at each time point to maintain the pH consistently at 3.5±0.2. The strategy is demonstrated for various buffers, columns, operating conditions, and process deviations in a three-column continuous process, and is a useful and simple approach for achieving robust control of pH at this critical point in the continuous train.

Near Infrared Spectroscopy as a PAT tool for monitoring and control of protein and excipient concentration in ultrafiltration of highly concentrated antibody formulations.

Excipient concentrations are critical quality attributes of monoclonal antibody (mAb) drug products and affect their safety and efficacy. In manufacturing processes, mAb products are formulated into the buffer containing the desired excipients using ultrafiltration (UF) and diafiltration (DF). Control of excipient concentrations is a challenge during high concentration UF due to electrostatic interactions which lead to excipient concentration drifts. This challenge is of increasing importance due to the growing preference towards high concentration subcutaneous drug formulations over conventional intravenous formulations in the biotherapeutic industry. Excipient concentrations are currently measured using offline RP-HPLC which is time-consuming and not suited for real time control. We propose a novel process analytical technology (PAT) tool for monitoring and control of mAb and excipients in high concentration UF using Near Infrared Spectroscopy (NIRS). The NIRS is able to monitor concentrations within ±1% for mAb and ±2% for two common excipients, L-histidine and acetate. A Python-based controller uses real time concentration data to deliver concentrated excipient stock solutions to the UF reservoir whenever the excipient concentrations drift out of range. The PAT control system is able to achieve the target formulation without manual intervention or at-line analysis and is well-suited for implementation in mAb manufacturing platforms.

Complete or periodic continuity in continuous manufacturing platforms for production of monoclonal antibodies?

BACKGROUND: Monoclonal antibodies (mAbs) currently dominate the biotherapeutic market. This has resulted in significant efforts towards the development of a continuous integrated platform for the manufacturing of mAbs. MAIN METHODS AND MAJOR RESULTS: In this study, a continuous mAb platform has been developed consisting of an Acoustic Wave Separator, a Cadence BioSMB PD system, a customized coiled flow reactor, a modular single-pass TFF kit, an in-line diafiltration module, and a continuous dead-end filtration skid. A three-step chromatographic purification was performed in the platform consisting of Protein A capture chromatography followed by an anion exchange membrane directly coupled to a cation exchange chromatography. Two operational case studies have been executed on the platform, namely complete continuous ("CC") and periodic continuous ("PC") modes of operation. The CC mode was designed to ensure that each unit operation had completely continuous inflow and outflow by increasing the number of columns, filtration modules and tanks, while the PC mode operated in periodic pulses with scheduled flow and hold steps. Both modes were designed to handle the same flow rate and titers from the upstream bioreactor or fed-batch harvest tank, and were compared in terms of productivity and operational complexity. Both modes offer viable options for continuous processing of mAbs and result in achievement of target critical quality attribute profiles of the final drug product over 24 h of operation. CONCLUSIONS AND IMPLICATIONS: It was found that the CC mode was superior in terms of specific productivity (20-50% higher) and consumable utilization (20% lower resin utilization), while the PC mode was operationally simpler and had lower facility costs due to significant reductions in the number of auxiliary equipment (pumps, columns, tanks, and valves). The work successfully highlighted the pros and cons of both approaches, and demonstrates that while several groups have amply shown the superiority of continuous processing over batch mode, there are intermediate variants which may be optimal in a given situation.

Control of surge tanks for continuous manufacturing of monoclonal antibodies.

Surge tanks are critical but often overlooked enablers of continuous bioprocessing. They provide multiple benefits including dampening of concentration gradients and allowing process resumption efforts in case of equipment failure or unexpected deviations, which can occur during a continuous campaign of weeks or months. They are also useful in enabling steady-state operation across a continuous train by facilitating mass balance between unit operations such as chromatography which have periodic loading and elution cycles. In this paper, we propose a design of a system of surge tanks for a monoclonal antibody (mAb) production process consisting of cell culture, clarification, capture chromatography, viral inactivation, polishing chromatography, and single-pass ultrafiltration and diafiltration. A Python controller has been developed for robust control of the continuous train. The controller has four layers, namely data acquisition, process scheduling, deviation handling, and real-time execution. A set of general guidelines for surge tank placement and sizing have been proposed together with process control strategies based on the design space of the individual unit operations, failure modes analysis of the different equipment, and expected variability in the process feed streams for both fed-batch and perfusion bioreactors. The control system has been successfully demonstrated for several continuous runs of up to 36 h in duration and is able to leverage surge tanks for robust control of the continuous train in the face of product variability as well as process errors while maintaining critical quality attributes. The proposed set of strategies for surge tank control are adaptable to most continuous processing setups for mAbs, and together form the first framework that can fully realize the benefits of surge tanks in continuous bioprocessing.

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.

Process analytical technology application for protein PEGylation using near infrared spectroscopy: G-CSF as a case study.

Conjugation of protein therapeutics with polymers like polyethylene glycol (PEG) has been shown to increase their therapeutic efficiency. However, manufacturing of PEGylated drugs requires an additional, carefully controlled reaction step after purifying the protein, followed by further purification of over- and under-PEGylated variants. In this work, we have used a combined spectroscopic and statistical approach for monitoring and control of the PEGylation reaction for G-CSF using near infrared spectroscopy (NIRS). An online NIRS probe deployed in the reaction vessel has been used to track conversion of G-CSF into monoPEGylated and multiPEGylated forms using calibrated partial least squares regression models on the NIRS spectra which are collected in real time every 3 s. A pH probe integrated with a peristaltic pump facilitates automated quenching of the reaction at the targeted time. The NIRS spectra have also been used to build a batch evolution model for the reaction from end-to-end, including the addition of the reactants to the reaction vessel, the progress of the reaction for 70 min, and the final quenching with Tris base. Online spectra are compared against the statistical process control charts of the batch evolution model in real time to detect deviations as soon as they occur. The system was demonstrated for four common deviations in the PEGylation process, namely: delayed quenching time, wrong concentration of reducing agent added, wrong PEG to G-CSF ratio, and wrong sequence of addition of reactants. The system was able to identify all four deviations in real time and alert the operator to take control actions. The PAT approach suggested here embraces the quality by design framework and can be generalized for manufacturing scale monitoring and control of different biotechnology reactions with spectroscopic signatures.

Automation of Dead End Filtration: An Enabler for Continuous Processing of Biotherapeutics.

Dead end filtration is a critical unit operation that is used for primary and secondary clarification during manufacturing of both microbial and mammalian cell based biotherapeutics. Dead end filtration is conventionally done in batch mode and requires filter pre-sizing using extensive scouting studies, along with filter over-sizing before deployment to handle potential variability. However, continuous manufacturing processes require consistent use of dead-end filtration over weeks or months, with potential unpredictable variations in feed stream attributes, which is a challenge currently facing the industry. In this work, a dead-end filtration skid is designed for continuous depth filtration, incorporating multiple small-sized filters along with turbidity, and pressure sensors with immediate switching to a fresh filter whenever turbidity or pressure breakthrough above a pre-determined cut-off is detected in real time. The skid has been successfully tested for manufacturing of granulocyte colony stimulating factor from Escherichia coli, human serum albumin from Pichia pastoris, and a monoclonal antibody therapeutic from CHO cells. The proposed skid can be directly applied for any dead-end filtration application with minimal prior scouting studies or sizing calculations for scale-up. It is a useful solution for continuous processing trains where the nature of the feed, such as its turbidity or host cell proteins content, may change over long continuous campaigns, rendering previous sizing calculations inaccurate. The skid also allows significant cost savings by eliminating the sizing safety factor of 1.5-2x which is generally added before filter deployment at manufacturing scale.

An NIR-based PAT approach for real-time control of loading in Protein A chromatography in continuous manufacturing of monoclonal antibodies.

Control of column loading in Protein A chromatography is a crucial part of development of robust and flexible process platforms for continuous production of monoclonal antibody (mAb) products. In this paper, we propose a control system that uses near infrared spectroscopy (NIRS) flow cells to accomplish the above. Two applications have been demonstrated using a periodic counter-current continuous chromatography setup. The first application involves use of single NIR flow cell before the inlet of the loading column to measure the concentration of mAb in the harvested broth. Measurement was in real-time (every 3 s) and within ±0.05 mg/ml, significantly better than making UV-based concentration estimations. The second application involved use of an additional NIR flow cell at the outlet of the loading column to measure column breakthrough in real time. The concentration data was transferred to a Python-based monitoring and control algorithm layered over a Cadence BioSMB system. The program could successfully run a three-column periodic counter current method on the BioSMB whereas controlling loading to ensure optimal resin utilization in each loading cycle phase based on precharacterized dynamic binding capacity models, whereas maintaining periodic elutions. The system was tested with multiple perturbations in harvest concentration, modeled after deviations that could arise downstream of a perfusion cell culture system. The results show that the proposed control is a spectroscopy-based process analytical technology tool that facilitates real time monitoring and control of loading in process chromatography. It is adaptable to any continuous chromatography equipment and is very well suited for implementation in a continuous mAb production train.

Process analytical technology implementation for protein refolding: GCSF as a case study.

Process analytical technology is gaining interest in the biopharmaceutical industry as a means to enable consistency in processing and thereby in product quality via process control. Protein refolding is known to be significantly impacted by critical process parameters and feed material attributes including composition and pH of the solubilisation and refolding buffers. Hence, to achieve robust process control and product quality, these attributes and parameters need to be monitored. This paper presents an approach towards statistical process control and monitoring of protein refolding, from buffer preparation to refold quenching, during manufacturing of therapeutic proteins from Escherichia coli based systems. The proposed approach utilises measurements of online redox potential, temperature, and pH for development of a statistical model. The model has then been integrated with LabView to permit real-time monitoring of the refolding process. The proposed system has been demonstrated to successfully identify process deviations and thereby enable process control for manufacturing product of consistent quality.